power_interaction {InteractionPoweR}

Power analysis for interactions

Description

Power analysis for interaction models, by simulation. A set of n.iter simulations is run for each unique combination of model settings.

Usage

power_interaction(
  n.iter,
  N,
  r.x1.y,
  r.x2.y,
  r.x1x2.y,
  r.x1.x2,
  rel.x1 = 1,
  rel.x2 = 1,
  rel.y = 1,
  k.x1 = 0,
  k.x2 = 0,
  k.y = 0,
  adjust.correlations = TRUE,
  alpha = 0.05,
  q = 2,
  cl = NULL,
  ss.IQR = 1.5,
  N.adjustment = 1e+06,
  detailed_results = FALSE,
  full_simulation = FALSE,
  tol = 0.005,
  iter = 10,
  skew.x1 = NA,
  skew.x2 = NA,
  skew.y = NA
)

Arguments

n.iter

Number of iterations. The number of simulations to run for each unique setting combination. Must be a positive integer.

N

Sample size. Must be a positive integer. Has no default value. Can be a single value or a vector of values.

r.x1.y

Pearson's correlation between x1 and y. Must be between -1 and 1.. Has no default value. Can be a single value or a vector of values.

r.x2.y

Pearson's correlation between x2 and y. Must be between -1 and 1.. Assumed to be the 'moderator' in some functions. Has no default value. Can be a single value or a vector of values.

r.x1x2.y

Pearson's correlation between the interaction term x1x2 (x1 * x2) and y. Must be between -1 and 1.. Has no default value. Can be a single value or a vector of values.

r.x1.x2

Pearson's correlation between x1 and x2. Must be between -1 and 1.. Has no default value. Can be a single value or a vector of values.

rel.x1

Reliability of x1 (e.g. test-retest reliability, ICC, Cronbach's alpha). Default is 1 (perfect reliability). Must be greater than 0 and less than or equal to 1.

rel.x2

Reliability of x2 (e.g. test-retest reliability, ICC, Cronbach's alpha). Default is 1 (perfect reliability). Must be greater than 0 and less than or equal to 1.

rel.y

Reliability of xy (e.g. test-retest reliability, ICC, Cronbach's alpha). Default is 1 (perfect reliability). Must be greater than 0 and less than or equal to 1.

k.x1

Number of discrete values for x1. Can be used to make a variable binary or ordinal.

k.x2

Number of discrete values for x2. Can be used to make a variable binary or ordinal.

k.y

Number of discrete values for y. Can be used to make a variable binary or ordinal.

adjust.correlations

If variables are ordinal or binary, should correlations be adjusted so that output data has the specified correlation structure? Default is TRUE.

alpha

The alpha. At what p-value is the interaction deemed significant? Default is 0.05.

q

Simple slopes. How many quantiles should x2 be split into for simple slope testing? Default is 2. Simple slope testing returns the effect-size (slope) of y~x1 for the two most extreme quantiles of x2. If q=3 then the two slopes are y~x1 for the bottom 33% of x2, and the top 33% of x2.

cl

Number of clusters to use for running simulations in parallel (recommended). Default is 1 (i.e. not in parallel).

ss.IQR

Simple slope IQR. Multiplier when estimating the distribution of simple slopes within each simulation setting. Default is 1.5.

N.adjustment

Sample size for simulations where correlation matrix is corrected to allow for binary/ordinal variables. Default is 1000000

detailed_results

Default is FALSE. Should detailed results be reported?

full_simulation

Default is FALSE. If TRUE, will return a list that includes the full per-simulation results.

tol

Correlation adjustment tolerance. When adjust.correlations = TRUE, correlations are adjusted so that the population correlation is within r='tol' of the target. Default = 0.005.

iter

Max number of iterations to run the correlation adjustment for. Typically only a couple are needed. Default = 10.

skew.x1

No longer supported.

skew.x2

No longer supported.

skew.y

No longer supported.

Value

A data frame containing the power (% significant results) for each unique setting combination. If full_simulation = TRUE will return a list, with one data frame that includes power, and a second that includes raw simulation results.

Examples

power_interaction(n.iter=10, N=10,r.x1.y=0.2, r.x2.y=.2,r.x1x2.y=0.5,r.x1.x2=.2)